SoroushVahidi/scidocs huggingface dataset:
Entropy-Based Subword Mining with an Application to Word Embeddings
(2018)

Recent literature has shown a wide variety of benefits to mapping traditional onehot representations of words and phrases to lower-dimensional real-valued vectors known as word embeddings. Traditionally, most word embedding algorithms treat each word as the finest meaningful semantic granularity and perform embedding by learning distinct embedding vectors for each word. Contrary to this line of thought, technical domains such as scientific and medical literature compose words from subword structures such as prefixes, suffixes, and root-words as well as compound words. Treating individual words as the finest-granularity unit discards meaningful shared semantic structure between words sharing substructures. This not only leads to poor embeddings for text corpora that have longtail distributions, but also heuristic methods for handling out-of-vocabulary words. In this paper we propose SubwordMine, an entropybased subword mining algorithm that is fast, unsupervised, and fully data-driven. We show that this allows for great cross-domain performance in identifying semantically meaningful subwords. We then investigate utilizing the mined subwords within the FastText embedding model and compare performance of the learned representations in a downstream language modeling task.
High throughput ultralong (20 cm) nanowire fabrication using a wafer-scale nanograting template.
(2013)

Nanowires are being actively explored as promising nanostructured materials for high performance flexible electronics, biochemical sensors, photonic applications, solar cells, and secondary batteries. In particular, ultralong (centimeter-long) nanowires are highly attractive from the perspective of electronic performance, device throughput (or productivity), and the possibility of novel applications. However, most previous works on ultralong nanowires have issues related to limited length, productivity, difficult alignment, and deploying onto the planar substrate complying with well-matured device fabrication technologies. Here, we demonstrate a highly ordered ultralong (up to 20 cm) nanowire array, with a diameter of 50 nm (aspect ratio of up to 4,000,000:1), in an unprecedented large (8 in.) scale (2,000,000 strands on a wafer). We first devised a perfectly connected ultralong nanograting master template on the whole area of an 8 in. substrate using a top-down approach, with a density equivalent to that achieved with e-beam lithography (100 nm). Using this large-area, ultralong, high-density nanograting template, we developed a fast and effective method for fabricating up to 20 cm long nanowire arrays on a plastic substrate, composed of metal, dielectric, oxide, and ferroelectric materials. As a suggestion of practical application, a prototype of a large-area aluminum wire grid polarizer was demonstrated.
pubmed.mineR: An R package with text-mining algorithms to analyse PubMed abstracts
(2015)

The PubMed literature database is a valuable source of information for scientific research. It is rich in biomedical literature with more than 24 million citations. Data-mining of voluminous literature is a challenging task. Although several text-mining algorithms have been developed in recent years with focus on data visualization, they have limitations such as speed, are rigid and are not available in the open source. We have developed an R package, pubmed.mineR, wherein we have combined the advantages of existing algorithms, overcome their limitations, and offer user flexibility and link with other packages in Bioconductor and the Comprehensive R Network (CRAN) in order to expand the user capabilities for executing multifaceted approaches. Three case studies are presented, namely, ‘Evolving role of diabetes educators’, ‘Cancer risk assessment’ and ‘Dynamic concepts on disease and comorbidity’ to illustrate the use of pubmed.mineR. The package generally runs fast with small elapsed times in regular workstations even on large corpus sizes and with compute intensive functions. The pubmed.mineR is available at http://cran.r-project.org/web/packages/pubmed.mineR .
Visualization Criticism - The Missing Link Between Information Visualization and Art
(2007)

Classifications of visualization are often based on technical criteria, and leave out artistic ways of visualizing information. Understanding the differences between information visualization and other forms of visual communication provides important insights into the way the field works, though, and also shows the path to new approaches. We propose a classification of several types of information visualization based on aesthetic criteria. The notions of artistic and pragmatic visualization are introduced, and their properties discussed. Finally, the idea of visualization criticism is proposed, and its rules are laid out. Visualization criticism bridges the gap between design, art, and technical/pragmatic information visualization. It guides the view away from implementation details and single mouse clicks to the meaning of a visualization.
Automating the Design of Graphical Presentations of Relational Information
(1986)

The goal of the research described in this paper is to develop an application-independent presentation tool that automatically designs effective graphical presentations (such as bar charts, scatter plots, and connected graphs) of relational information. Two problems are raised by this goal: The codification of graphic design criteria in a form that can be used by the presentation tool, and the generation of a wide variety of designs so that the presentation tool can accommodate a wide variety of information. The approach described in this paper is based on the view that graphical presentations are sentences of graphical languages. The graphic design issues are codified as expressiveness and effectiveness criteria for graphical languages. Expressiveness criteria determine whether a graphical language can express the desired information. Effectiveness criteria determine whether a graphical language exploits the capabilities of the output medium and the human visual system. A wide variety of designs can be systematically generated by using a composition algebra that composes a small set of primitive graphical languages. Artificial intelligence techniques are used to implement a prototype presentation tool called APT (A Presentation Tool), which is based on the composition algebra and the graphic design criteria.
Parallel Coordinates: A Tool for Visualizing Multi-dimensional Geometry
(1990)

A methodology for visualizing analytic and synthetic geometry in RN is presented. It is based on a system of parallel coordinates which induces a non-projective mapping between N-Dimensional and 2-Dimensional sets. Hypersurfaces are represented by their planar images which have some geometrical properties analogous to the properties of the hypersurface that they represent. A point ← → line duality when N = 2 generalizes to lines and hyperplanes enabling the representation of polyhedra in RN. The representation of a class of convex and non-convex hypersurfaces is discussed together with an algorithm for constructing and displaying any interior point. The display shows some local properties of the hypersurface and provides information on the point's proximity to the boundary. Applications to Air Traffic Control, Robotics, Computer Vision, Computational Geometry, Statistics, Instrumentation and other areas are discussed.
Visualizing the non-visual: spatial analysis and interaction with information from text documents
(1995)

The paper describes an approach to IV that involves spatializing text content for enhanced visual browsing and analysis. The application arena is large text document corpora such as digital libraries, regulations and procedures, archived reports, etc. The basic idea is that text content from these sources may be transformed to a spatial representation that preserves informational characteristics from the documents. The spatial representation may then be visually browsed and analyzed in ways that avoid language processing and that reduce the analysts mental workload. The result is an interaction with text that more nearly resembles perception and action with the natural world than with the abstractions of written language.
Accelerating fish detection and recognition by sharing CNNs with objectness learning
(2016)

Daily increasing underwater visual data makes automatic object detection and recognition a great demand and challenging task. In this paper, we adopt a region proposal network to accelerate underwater object detection and recognition from Faster R-CNN. This process implement detection acceleration by using convolutional networks to generate high-quality object candidates, and by sharing these networks with original detection networks. Applied to a real-world fish dataset, which includes 24,277 ImageCLEF fish images belonging to 12 classes, this automatic detection and recognition system has a nearly real-time frame rate of 9.8 ftps, while yielding 15.1% higher Mean Average Precision (mAP) than the Deformable Parts Model (DPM) baseline.
Image annotation via deep neural network
(2015)

Multilabel image annotation is one of the most important open problems in computer vision field. Unlike existing works that usually use conventional visual features to annotate images, features based on deep learning have shown potential to achieve outstanding performance. In this work, we propose a multimodal deep learning framework, which aims to optimally integrate multiple deep neural networks pretrained with convolutional neural networks. In particular, the proposed framework explores a unified two-stage learning scheme that consists of (i) learning to fune-tune the parameters of deep neural network with respect to each individual modality, and (ii) learning to find the optimal combination of diverse modalities simultaneously in a coherent process. Experiments conducted on the NUS-WIDE dataset evaluate the performance of the proposed framework for multilabel image annotation, in which the encouraging results validate the effectiveness of the proposed algorithms.
Procrastination and cramming: how adept students ace the system.
(1990)

Clear power differentials between teacher and pupil and the assignment of well-delineated tasks within specified time constraints characterize the academic system. Most university students are supremely adept at working within that structure. Many students who outwardly adapt to the system, however, engage in an intense and private ritual that comprises five aspects: calculated procrastination, preparatory anxiety, climactic cramming, nick-of-time deadline-making, and a secret, if often uncelebrated, victory. These adept students often find it difficult to admit others into their efficient program of academic survival. Although such behaviors are adaptive for school rhythms and expectations, these students may also try to impose them onto personal relationships, including those that are psychotherapeutic. The students' tendency to transfer their longstanding habits of procrastination and cramming to the workplace after graduation is less problematic. The argument is made that school rhythms and expectations shape the workplace, not vice versa. Methods of addressing the troublesome aspects of these work styles are suggested. Rather than attempting to change existing work patterns, the therapist can identify the underlying psychodynamic drama and its attendant manifestations that are becoming problematic.
An efficient FPGA overlay for portable custom instruction set extensions
(2013)

Custom instruction set extensions can substantially boost performance of reconfigurable softcore CPUs. While this approach is commonly tailored to one specific FPGA system, we are presenting a fine-grained FPGA-like overlay architecture which can be implemented in the user logic of various FPGA families from different vendors. This allows the execution of a portable application consisting of a program binary and an overlay configuration in a completely heterogeneous environment. Furthermore, we are presenting different optimizations for dramatically reducing the implementation cost of the proposed overlay architecture. In particular, this includes the mapping of the overlay interconnection network directly into the switch fabric of the hosting FPGA. Our case study demonstrates an overhead reduction of an order of magnitude as compared to related approaches.
What a Nasty Day: Exploring Mood-Weather Relationship from Twitter
(2014)

While it has long been believed in psychology that weather somehow influences human's mood, the debates have been going on for decades about how they are correlated. In this paper, we try to study this long-lasting topic by harnessing a new source of data compared from traditional psychological researches: Twitter. We analyze 2 years' twitter data collected by twitter API which amounts to 10% of all postings and try to reveal the correlations between multiple dimensional structure of human mood with meteorological effects. Some of our findings confirm existing hypotheses, while others contradict them. We are hopeful that our approach, along with the new data source, can shed on the long-going debates on weather-mood correlation.
Object detection by spatio-temporal analysis and tracking of the detected objects in a video with variable background
(2019)

In this paper we propose a novel approach for detecting and tracking objects in videos with variable background i.e. videos captured by moving cameras without any additional sensor. The performance of tracking in videos with variable background depends on the successful detection of an object in variable background. The most attractive feature of detecting an object in variable background is that it does not depend on any a priori information of the scene. In a video captured by a moving camera, both the background and foreground are changing in each frame of the image sequence. So for these videos, modeling a single background with traditional background modeling methods is infeasible and thus the detection of actual moving object in a variable background is a challenging task. To detect actual moving object in this work, spatio-temporal blobs have been generated in each frame by spatio-temporal analysis of the image sequence using a three-dimensional Gabor filter. Then individual blobs, which are parts of one object are merged using Minimum Spanning Tree to form the moving object in the variable background. The height, width and four-bin grayvalue histogram of the object are calculated as its features and an object is tracked in each frame using these features to generate the trajectories of the object through the video sequence. In this work, problem of data association during tracking is solved by Linear Assignment Problem and occlusion is handled by the application of kalman filter. The major advantage of our method over most of the existing tracking algorithms is that, the proposed method does not require initialization in the first frame or training on sample data to perform. Performance of the algorithm has been tested on benchmark videos and very satisfactory result has been achieved. The performance of the algorithm is also comparable and superior with respect to some benchmark algorithms.
The Influence of Information Overload on the Development of Trust and Purchase Intention Based on Online Product Reviews in a Mobile vs . Web Environment : A Research Proposal
(2017)

Information overload has been studied extensively by decision science researchers, particularly in the context of task-based optimization decisions. Media selection research has similarly investigated the extent to which task characteristics influence media choice and use. This paper outlines a proposed study, which would compare the effectiveness of web-based online product review systems in facilitation trust and purchase intention to those of mobile product review systems. We propose that since web-based systems are more effective in fostering focus and are less prone to navigation frustration, information overload is less likely influence the extent to which a consumer trusts an online product review.
Real-time detection of planar regions in unorganized point clouds
(2015)

Automatic detection of planar regions in point clouds is an important step for many graphics, image processing, and computer vision applications. While laser scanners and digital photography have allowed us to capture increasingly larger datasets, previous techniques are computationally expensive, being unable to achieve real-time performance for datasets containing tens of thousands of points, even when detection is performed in a non-deterministic way. We present a deterministic technique for plane detection in unorganized point clouds whose cost is O(n log n) in the number of input samples. It is based on an efficient Hough-transform voting scheme and works by clustering approximately co-planar points and by casting votes for these clusters on a spherical accumulator using a trivariate Gaussian kernel. A comparison with competing techniques shows that our approach is considerably faster and scales significantly better than previous ones, being the first practical solution for deterministic plane detection in large unorganized point clouds.
AVATAR: A Framework to Support Dynamic Security Analysis of Embedded Systems' Firmwares
(2014)

To address the growing concerns about the security of embedded systems, it is important to perform accurate analysis of firmware binaries, even when the source code or the hardware documentation are not available. However, research in this field is hindered by the lack of dedicated tools. For example, dynamic analysis is one of the main foundations of security analysis, e.g., through dynamic taint tracing or symbolic execution. Unlike static analysis, dynamic analysis relies on the ability to execute software in a controlled environment, often an instrumented emulator. However, emulating firmwares of embedded devices requires accurate models of all hardware components used by the system under analysis. Unfortunately, the lack of documentation and the large variety of hardware on the market make this approach infeasible in practice. In this paper we present Avatar, a framework that enables complex dynamic analysis of embedded devices by orchestrating the execution of an emulator together with the real hardware. We first introduce the basic mechanism to forward I/O accesses from the emulator to the embedded device, and then describe several techniques to improve the system’s performance by dynamically optimizing the distribution of code and data between the two environments. Finally, we evaluate our tool by applying it to three different security scenarios, including reverse engineering, vulnerability discovery and hardcoded backdoor detection. To show the flexibility of Avatar, we perform this analysis on three completely different devices: a GSM feature phone, a hard disk bootloader, and a wireless sensor node.
Optimization of Helical Antennas
(2007)

Helical antennas have been known for a long time, but the literature is overwhelmed with controversial information about their performance. We have systematically investigated helical antennas located above an infinite ground plane and obtained design curves. We have also observed that the shape and size of the ground conductor have influence on the helical antenna performance. By optimizing the dimensions of ground conductors that have the form of a cup and a cone, we have significantly increased the antenna gain. Simultaneous optimization of the helix and the ground conductor is under way to improve the antenna performance.
Correlated discrete data generation using adversarial training
(2018)

Generative Adversarial Networks (GAN) have shown great promise in tasks like synthetic image generation, image inpainting, style transfer, and anomaly detection. However, generating discrete data is a challenge. This work presents an adversarial training based correlated discrete data (CDD) generation model. It also details an approach for conditional CDD generation. The results of our approach are presented over two datasets; job-seeking candidates skill set (private dataset) and MNIST (public dataset). From quantitative and qualitative analysis of these results, we show that our model performs better as it leverages inherent correlation in the data, than an existing model that overlooks correlation.
HotFlip: White-Box Adversarial Examples for NLP
(2017)

We propose an efficient method to generate white-box adversarial examples to trick a character-level neural classifier. We find that only a few manipulations are needed to greatly decrease the accuracy. Our method relies on an atomic flip operation, which swaps one token for another, based on the gradients of the onehot input vectors. Due to efficiency of our method, we can perform adversarial training which makes the model more robust to attacks at test time. With the use of a few semantics-preserving constraints, we demonstrate that HotFlip can be adapted to attack a word-level classifier as well.
Experiences Using the RISC-V Ecosystem to Design an Accelerator-Centric SoC in TSMC 16 nm
(2017)

The recent trend towards accelerator-centric architectures has renewed the need for demonstrating new research ideas in prototype systems with custom chips. Unfortunately, building such research prototypes is tremendously challenging, but the emerging RISC-V open-source software and hardware ecosystem can partly address this challenge by reducing design, implementation, and verification effort. This paper briefly describes the Celerity system-on-chip (SoC), a 5× 5mm 385M-transistor chip in TSMC 16 nm, which uses a tiered parallel accelerator fabric to improve both the performance and energy efficiency of embedded applications. The Celerity SoC includes five RV64G cores, a 496-core RV32IM tiled manycore processor, and a complex BNN (binarized neural network) accelerator implemented as a Rocket custom co-processor (RoCC). We describe our experiences using the RISC-V ecosystem to build Celerity, and highlight both key benefits and challenges in leveraging the RISCV instruction set, RISC-V software stack, RISC-V processor and memory system generators, RISC-V on-chip network interfaces, RISC-V verification suite, and RISC-V system-level hardware infrastructure. The RISC-V ecosystem played an important role in enabling a team of junior graduate students to design and tapeout the highest-performance RISC-V SoC to date in just nine months.
Relation Networks for Object Detection
(2018)

Although it is well believed for years that modeling relations between objects would help object recognition, there has not been evidence that the idea is working in the deep learning era. All state-of-the-art object detection systems still rely on recognizing object instances individually, without exploiting their relations during learning. This work proposes an object relation module. It processes a set of objects simultaneously through interaction between their appearance feature and geometry, thus allowing modeling of their relations. It is lightweight and in-place. It does not require additional supervision and is easy to embed in existing networks. It is shown effective on improving object recognition and duplicate removal steps in the modern object detection pipeline. It verifies the efficacy of modeling object relations in CNN based detection. It gives rise to the first fully end-to-end object detector.